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5 Tips to Simulate Faster in SimScale

FEM and CFD simulations have grown to be reliable over the years. Yet, the biggest drawback has remained the computing times when the problems involve nonlinearities or transient phenomenon. Real-time FEM, especially for surgical applications, has shown great potential but remained an academic technology primarily due to the computational times. In this article, we discuss five important tips to simulate faster using SimScale.

Faster Simulations Through Symmetry

Many problems, when idealized, involve symmetry. This symmetry could be along an axis or could be about a line. Using symmetry is one of the very common and yet powerful ways of reducing the overall size of the problem and hence increasing the speed of the simulations. Symmetry is said to exist not only in the geometry but could also in the loads and constraints about a line or a plane of symmetry.

Symmetry conditions

Fig 01: Symmetry in solid bodies: A plate with a hole subjected to uniaxial tensile loading (left) and a cylinder in front-view subjected to radial pressure (right)

As shown in Fig. 01, symmetry is most often found in nature. The same plate with a hole could also be a matrix with a particle. In such a scenario, the hole can be replaced by the particle material. Depending on the genre of the problem, considering symmetry would mean that the simulation runs 3-4 times faster than otherwise.

Consider Mesh Refinement

A good mesh can go a long way in making life very easy and simulations faster. There are several aspects to consider when meshing. A mesh convergence analysis is very much necessary to be sure that the obtained results are accurate. One of the ways to ensure a quality mesh is through mesh refinement. SimScale offers mesh refinement option during mesh. This allows selected regions to be refined in comparison to refining the entire model and thus ensuring faster simulations.

Mesh Refinement

Fig 02: Mesh refinement of surfaces for improved accuracy in solutions. CAD model (left), Areas needing mesh refinement identified in red (middle), Mesh incorporating refinement (right)

Similarly, in the presence of sharp points and corners, the mesh needs to be refined in these regions and these regions can be selected to ensure mesh refinement as shown above.

Most often the CAD models obtained are obtained from online repositories like GrabCAD etc. We advise caution in using these, especially for fluid mechanics simulations. The outer surfaces most often have engravings of copyrights or names of the authors. Such small engravings can result in small surfaces that significantly affect the simulation and increases the computational times. It is recommended that these models are checked for such small surfaces to ensure faster simulations.

A more detailed analysis of meshing in structural mechanics problems is found in this blog article: “How to Mesh your CAD Model for Structural Analysis (FEA)“.

Stability of Material Parameters

Another important aspect to be considered, in structural simulations, is the stability of the material model or parameters considered. Material parameters that are not unconditionally stable is the reason why structural simulations fail in 90% of the cases. The material parameters are generally obtained by fitting the experimental data. The fitting is limited by the maximum stretch to which the data is available. If the model is used beyond this limit, the model might not be unconditionally stable. In such a case, each step in the simulation can take longer to converge and hence slower.

Non-uniqueness in force-driven problems

Fig 03: Instability created in force-displacement diagram due to material instabilities

For example: Say if the experimental data was only available for a stretch to 30% and the model parameters were fitted with this. When a simulation is done using this model where the strains are much larger (> 30%), it is possible that the nonlinear behavior beyond 30% is not accurately captured.

Secondly, depending on the number of tests used for fitting, it is possible that the material demonstrates an instability when subjected to a different type of loading. For example: Say material parameters for Mooney-Rivlin model are fitted using only uniaxial tests. When a biaxial simulation is done, it is possible that the results show significant instabilities.

Such instabilities can be identified by plotting the force-displacement diagram on simple tests performed using the chosen material parameters. Identifying such instabilities through simple tests like uniaxial, biaxial, shear, volumetric tests could significantly help in creating faster simulations.

Substitute Nonlinearity for Speed

Today, there are no more problems related to linear FEM that cannot be solved. One can say that any problem that does not have a nonlinearity i.e. material nonlinearity (like hyperelasticity or plasticity) or geometric nonlinearity (like large rotations in thin structures) or nonlinearity in boundary condition (like contact) can definitely be solved without battling an eyelid.

The biggest challenge for the coming decade is in solving nonlinear problems. In particular, contact is known to be the worst of the kind and is known as a “strong nonlinearity”. This means that it is like a switch – on or off. The original mathematics of FEM was based on smoothness but such a case where there are no intermediate state results in a jump. This is also one of the reasons that contact remains an enigma even after 300 years of studying. A more detailed discussion on contact can be found in the article “Contact Mechanics and Friction: Is CAE Shedding Light on the Problem?“.

Applications of contact

Fig 04: Applications of contact. From left: car crash, sheet metal forming, eccentric loading on building foundation, roller bearing, impact on bridge columns

Contact has several applications as shown in Fig. 04. There are no structural mechanics problems that do not involve contact. A good simplification to ensure faster simulations is to substitute contact with simpler conditions. If two surfaces are always in contact then, bonded contact could be a good option. Alternatively, force or displacement boundary conditions are often used to replace contact conditions. Depending on the problem of choice, one of these methods ensure faster convergence and simulation times.

Parallel Computing Using SimScale

Parallel computing has been under a veil of complexity for several years now. It was primarily an area of interest to computer engineers but rapidly utilized by simulation scientists. Yet, the term “Parallel Computing” itself is associated with terms like “Message Passing Interface” etc that makes the situation claustrophobic for many mechanical engineers.

SimScale offers a nice option to use parallel computing without having to worry about the internal workings. As shown in the Fig. 05, it can be seen that the number of computing cores can be easily set through a simple drop-down menu.

Simulate faster with parallel computing

Figure 05: Simulation control settings for a CFD (Left) and FEM (right) simulation showing the number of computing cores selection

Figure 05 shows that for both, CFD and FEM, simulations SimScale offers parallel computing with up to 32 cores. It is strongly recommended to use parallel computing, especially for large problems, to reduce the overall computing times.

Conclusion

There are several avenues more to explore to increase speed. Most often civil engineers use simple simulations to get an order of magnitude estimate. Further on, more detailed simulations are done on a reduced sample set. SimScale offers the possibility of running engineering simulations in parallel and this option enables you to get the best of what the platform has to offer.


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